April 02, 2018
Garrett
4/02/2018
Read and organize data
read_srm_export <- function(filename, columns = c("peak_name", "RT.min", "basepeak", "area.cpm", "height.cts", "quantitation")) {
filename %>%
# read excel files
read_excel(sheet = "Integration", skip = 42,
col_names = columns, col_types = rep("text", length(columns))) %>%
as_data_frame() %>%
# remove empty rows
filter(!is.na(peak_name), peak_name != "n.a.") %>%
# convert the relevant numeric columns into numbers
mutate_at(vars(RT.min, area.cpm, height.cts), as.numeric) %>%
# remove useless columns
select(-basepeak, -quantitation) %>%
# add filename info
mutate(file_id = gsub("\\.xls", "", basename(filename))) %>%
select(file_id, everything())
}
# get data
all_data <-
# find all excel files
list.files("data_reports", recursive = TRUE, full.names = TRUE, pattern = "\\.xls$") %>%
# send them to the read method
lapply(read_srm_export) %>%
# combine the data set
bind_rows() %>%
# pull out sample information
#mutate(sample_id = str_match(all_data$file_id, "TSQ\\d+_GB_(.*)$") %>% { .[,2] }) %>%
# get n replicates
group_by(file_id)
#mutate(n_replicates = length(unique(file_id)))File names for metadata file
Calculation peak amounts and rock concentrations
depth_and_rock_info <- read_excel(file.path("metadata", "aromaticSRM_20180327.xlsx")) %>%
rename(rock.g = `rock .g`) %>%
filter(!is.na(file_id))
kable(depth_and_rock_info)| file_id | depth | rock.g | process | TLE.mg | maltenes.mg |
|---|---|---|---|---|---|
| TSQ1998_GB_OG043 | 122.900 | 11.349 | yes | 14.7 | 11.9 |
| TSQ2001_GB_OG058 | 121.195 | 10.711 | yes | 18.4 | 4.2 |
| TSQ2002_GB_OG063 | 122.390 | 10.116 | yes | 139.5 | 8.8 |
| TSQ2003_GB_OG043 | 122.900 | 11.349 | yes | 14.7 | 11.9 |
| TSQ2004_GB_OG053 | 122.805 | 10.786 | yes | 71.2 | 6.7 |
| TSQ2005_GB_OG054 | 122.700 | 12.054 | yes | 77.3 | 13.5 |
| TSQ2006_GB_OG050 | 120.600 | 11.195 | yes | 16.1 | 2.1 |
| TSQ2008_GB_OG048 | 121.295 | 10.828 | yes | 14.5 | 4.4 |
| TSQ2009_GB_OG062 | 120.950 | 10.341 | yes | 83.1 | 7.8 |
| TSQ2010_GB_OG011 | 114.000 | 10.208 | yes | 0.4 | 6.9 |
| TSQ2011_GB_OG012 | 113.000 | 10.552 | yes | 9.4 | 11.4 |
| TSQ2012_GB_OG013 | 111.080 | 9.747 | yes | 6.9 | 9.5 |
| TSQ2013_GB_OG037 | 121.600 | 10.530 | yes | 5.2 | 3.6 |
| TSQ2014_GB_OG038 | 120.700 | 11.444 | yes | 3.3 | 2.2 |
| TSQ2015_GB_OG008 | 118.000 | 9.698 | yes | 3.4 | 5.0 |
| TSQ2016_GB_OG040 | 121.890 | 10.300 | yes | 6.5 | 3.3 |
| TSQ2018_GB_OG041 | 120.405 | 11.393 | yes | 2.3 | 1.9 |
| TSQ2019_GB_OG042 | 120.205 | 10.757 | yes | NA | NA |
| TSQ2020_GB_OG045 | 120.205 | 10.757 | yes | 4.1 | 2.4 |
| TSQ2021_GB_OG044 | 120.300 | 11.390 | yes | 3.5 | 2.7 |
| TSQ2022_GB_OG046 | 121.500 | 11.191 | yes | 24.7 | 3.1 |
| TSQ2023_GB_OG047 | 121.105 | 10.868 | yes | 38.8 | 5.5 |
| TSQ2024_GB_OG051 | 120.900 | 14.166 | yes | 96.8 | 6.3 |
| TSQ2025_GB_OG052 | 121.800 | 10.268 | yes | 4.5 | 3.0 |
| TSQ2026_GB_OG057 | 122.500 | 10.939 | yes | 36.8 | 12.6 |
| TSQ2028_GB_OG059 | 122.090 | 9.486 | yes | 54.9 | 3.6 |
| TSQ2029_GB_OG060 | 122.200 | 11.367 | yes | 53.3 | 2.8 |
| TSQ2030_GB_OG061 | 121.400 | 12.044 | yes | 40.7 | 4.0 |
| TSQ2031_GB_OG015 | 109.100 | 10.984 | yes | 13.3 | 15.3 |
| TSQ2032_GB_OG007 | 119.000 | 9.620 | yes | -214.2 | 4.8 |
| TSQ2033_GB_OG010 | 115.000 | 11.070 | yes | 5.0 | 6.0 |
| TSQ2034_GB_OG009 | 117.010 | 10.742 | yes | 5.0 | 6.3 |
| TSQ2035_GB_OG003 | 124.000 | 10.120 | yes | 224.4 | 7.3 |
| TSQ2036_GB_OG005 | 122.000 | 9.280 | yes | 3.2 | 5.4 |
| TSQ2038_GB_OG002 | 125.015 | 10.001 | yes | 24.9 | 13.7 |
| TSQ2039_GB_OG039 | 120.100 | 7.997 | yes | 0.8 | 0.9 |
| TSQ2044_GB_OG016 | 108.300 | 10.089 | no | 5.1 | NA |
| TSQ2045_GB_OG017 | 106.900 | 10.348 | yes | -6.2 | 6.8 |
| TSQ2046_GB_OG018 | 106.090 | 10.337 | yes | 4.9 | 6.5 |
| TSQ2047_GB_OG019 | 105.100 | 11.002 | yes | 2.2 | -5.2 |
| TSQ2048_GB_OG021 | 103.890 | 10.056 | yes | 21.6 | 7.4 |
| TSQ2049_GB_OG023 | 101.100 | 11.294 | yes | 8.5 | NA |
| TSQ2051_GB_OG024 | 100.090 | 9.879 | yes | 6.3 | 5.6 |
| TSQ2052_GB_OG025 | 98.900 | 10.713 | yes | 7.8 | 8.6 |
| TSQ2053_GB_OG026 | 98.090 | 9.336 | yes | 4.7 | 7.4 |
| TSQ2054_GB_OG027 | 96.890 | 9.378 | yes | 5.3 | 6.3 |
| TSQ2055_GB_OG028 | 96.090 | 9.758 | yes | 6.9 | 7.3 |
| TSQ2056_GB_OG029 | 95.080 | 11.469 | yes | 4.3 | 2.3 |
| TSQ2057_GB_OG030 | 94.560 | 10.195 | yes | 7.1 | 8.2 |
| TSQ2058_GB_OG031 | 119.950 | 7.502 | yes | 2.6 | 4.7 |
| TSQ2060_GB_OG033 | 112.070 | 7.287 | yes | 2.3 | -7.2 |
| TSQ2061_GB_OG022 | 101.900 | 11.025 | yes | 4.5 | 7.2 |
| TSQ2062_GB_OG014 | 110.100 | 10.273 | yes | 14.5 | 14.0 |
| TSQ2063_GB_OG032 | 115.885 | 9.032 | yes | -2.1 | 4.4 |
| 137 | 117.800 | 11.205 | yes | 14.0 | 16.4 |
| 138 | 123.600 | 10.512 | yes | 15.5 | 31.6 |
| 139 | 118.925 | 10.663 | yes | 8.3 | 8.6 |
| 140 | 124.600 | 11.468 | yes | 12.3 | 7.6 |
| 141 | 118.490 | 10.656 | yes | 8.1 | 7.3 |
| TSQ2221_GB_OG143 | 119.100 | 10.763 | yes | 284.8 | 5.0 |
| TSQ2222_GB_OG144 | 116.700 | 10.862 | yes | 10.6 | 9.5 |
| TSQ2223_GB_OG145 | 117.090 | 11.360 | yes | 9.5 | 2.8 |
| 146 | NA | 0.000 | no | 4.0 | 0.3 |
| TSQ2224_GB_OG147 SB | NA | 0.000 | no | 3.6 | 0.3 |
| TSQ2225_GB_OG148 | 117.210 | 10.792 | yes | 8.4 | 3.2 |
| TSQ2226_GB_OG149 | 117.700 | 10.463 | yes | 6.3 | 2.0 |
| TSQ2228_GB_OG150 | 117.700 | 10.393 | yes | 5.5 | 1.7 |
| TSQ2229_GB_OG151 | 117.900 | 11.828 | yes | 5.1 | 1.0 |
| TSQ2230_GB_OG152 | 117.280 | 11.463 | yes | 10.6 | 3.9 |
| TSQ2231_GB_OG153 | 116.810 | 11.177 | yes | 9.8 | 3.9 |
| TSQ2232_GB_OG154 | 116.600 | 12.080 | yes | 29.1 | 3.6 |
| TSQ2233_GB_OG155 | 119.850 | 11.042 | yes | 7.1 | 1.6 |
| TSQ2235_GB_OG156 | 117.400 | 11.244 | yes | 8.0 | 3.3 |
| TSQ2236_GB_OG157 | 118.800 | 11.346 | yes | 8.3 | 2.6 |
| TSQ2237_GB_OG158 | 119.550 | 11.853 | yes | 7.6 | 3.2 |
| TSQ2238_GB_OG159 | 119.655 | 11.044 | yes | 6.7 | 3.8 |
| TSQ2240_GB_OG160 | 118.300 | 11.305 | no | 7.4 | 2.1 |
| TSQ2242_GB_OG161 | 116.490 | 10.770 | yes | 8.4 | 2.9 |
| TSQ2243_GB_OG162 | 116.900 | 10.931 | no | 10.9 | 4.1 |
| TSQ2244_GB_OG163 | 119.445 | 11.636 | no | 1.8 | 1.4 |
| TSQ2215_GB_OG164 | 118.100 | 10.619 | yes | 7.6 | 3.5 |
| TSQ2216_GB_OG165 | 119.570 | 11.300 | yes | 5.0 | 1.6 |
| TSQ2217_GB_OG166 | 118.600 | 10.790 | yes | 7.1 | 1.9 |
| TSQ2218_GB_OG167 | 117.490 | 10.853 | yes | 8.8 | 2.9 |
| TSQ2219_GB_OG168 | 118.965 | 11.259 | yes | 6.3 | 2.0 |
data_by_depth <-
all_data %>%
left_join(depth_and_rock_info, by = "file_id") %>%
group_by(file_id) %>%
mutate(
n_peaks = n(),
n_standards = sum(peak_name == "d14-pTerph"),
ref_area.cpm = area.cpm[peak_name == "d14-pTerph"],
ref_amount_added.ug = 5, #check standard amount added
amount.ug = area.cpm/ref_area.cpm * ref_amount_added.ug,
conc_rock.ug_g = amount.ug / rock.g
#total_area.cpm = sum(area.cpm[peak_name != "d14-pTerph"]),
#area.percent = area.cpm / total_area.cpm * 100
)%>% ungroup() %>%
filter(`process` == "yes") %>%
arrange(file_id, peak_name)
data_by_depth## # A tibble: 4,705 x 16
## file_id peak_name RT.min area.cpm height.cts depth rock.g process
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
## 1 TSQ2001_G… 1-MP 26.1 9.69e⁶ 168777000 121 10.7 yes
## 2 TSQ2001_G… 2-MP 26.8 1.26e⁷ 228207000 121 10.7 yes
## 3 TSQ2001_G… 3-MP 26.7 1.47e⁷ 265019000 121 10.7 yes
## 4 TSQ2001_G… 9-MP 25.9 1.09e⁷ 198920000 121 10.7 yes
## 5 TSQ2001_G… Acenapthene 14.2 2.71e⁶ 56677300 121 10.7 yes
## 6 TSQ2001_G… Benzo[a]ant… 41.8 6.84e⁶ 105420000 121 10.7 yes
## 7 TSQ2001_G… Benzo[a]pyr… 51.8 1.51e⁷ 243142000 121 10.7 yes
## 8 TSQ2001_G… Benzo[b]flo… 49.7 1.25e⁷ 146978000 121 10.7 yes
## 9 TSQ2001_G… Benzo[e]pyr… 51.4 1.84e⁷ 300891000 121 10.7 yes
## 10 TSQ2001_G… Benzo[ghi] 60.0 1.66e⁸ 1312680000 121 10.7 yes
## # ... with 4,695 more rows, and 8 more variables: TLE.mg <dbl>,
## # maltenes.mg <dbl>, n_peaks <int>, n_standards <int>,
## # ref_area.cpm <dbl>, ref_amount_added.ug <dbl>, amount.ug <dbl>,
## # conc_rock.ug_g <dbl>Calculate Recovery
Linear regressions of the calibration curves
standard <- read_excel(file.path("metadata", "D14 calibration.xlsx")) ###read excel
###calibration curve
standard %>%
ggplot() +
aes(x = Known.ng, y = Measured_area.counts, color = calibration) +
geom_smooth(method = "lm", alpha = 0.5) +
geom_point() +
theme_bw() +
theme(legend.position = "none") 
calibrations <-
standard %>%
filter(!is.na(calibration)) %>%
nest(-calibration) %>%
mutate(
fit = map(data, ~summary(lm(`Measured_area.counts`~ `Known.ng`, data = .x))),
coefficients = map(fit, "coefficients"),
intercept = map_dbl(coefficients, `[`, 1, 1),
intercept_se = map_dbl(coefficients, `[`, 1, 2),
slope = map_dbl(coefficients, `[`, 2, 1),
slope_se = map_dbl(coefficients, `[`, 2, 2),
r2 = map_dbl(fit, "r.squared")
)
calibrations %>% select(-data, -fit, -coefficients) %>% knitr::kable(d = 3)| calibration | intercept | intercept_se | slope | slope_se | r2 |
|---|---|---|---|---|---|
| sept2017 | -131054.9 | 2131453 | 2275077 | 90941.37 | 0.997 |
| oct2017 | -1488460.7 | 2799385 | 1557777 | 107323.96 | 0.995 |
Determine yield
These numbers are not useful for anything else.
calib_data <-
data_by_depth %>%
# temp
mutate(calibration = "oct2017") %>%
left_join(calibrations, by = "calibration") %>%
mutate(
total_volume.uL = 100,
total_inject.uL = 1,
ref_amount_inject_expected.ng = ref_amount_added.ug/total_volume.uL * total_inject.uL * 1000,
ref_amount_inject_measured.ng = (ref_area.cpm - intercept)/slope,
ref_amount_measured.ug = total_volume.uL/total_inject.uL * ref_amount_inject_measured.ng * 1/1000,
yield = ref_amount_inject_measured.ng/ref_amount_inject_expected.ng
)check yields
calib_data %>%
select(file_id, yield) %>%
arrange(file_id) %>%
unique() %>%
ggplot() + aes(file_id, y = 100*yield) +
geom_point(size = 3) +
theme_bw() + theme(axis.text.x = element_text(angle = 90, hjust = 0, vjust = 0.5))
Combine compounds/make ratios (new rows w/o RTs etc, just concentration.rock column)
# functions to make it easy to sum up peaks
sum_peaks <- function(df, filter_condition, new_peak_name) {
filter_condition <- sprintf("(%s)", str_c(filter_condition, collapse = "|"))
filter(df, str_detect(peak_name, filter_condition)) %>%
summarize(
file_id = file_id[1],
depth = depth[1],
conc_rock.ug_g = sum(conc_rock.ug_g)
) %>%
mutate(peak_name = new_peak_name)
}
ratio_peaks <- function(df, filter_top, filter_bottom, new_peak_name) {
filter_top <- sprintf("(%s)", str_c(filter_top, collapse = "|"))
filter_bottom <- sprintf("(%s)", str_c(filter_bottom, collapse = "|"))
filter(df, str_detect(peak_name, filter_top) | str_detect(peak_name, filter_bottom)) %>%
summarize(
file_id = file_id[1],
depth = depth[1],
ratio = sum(conc_rock.ug_g[str_detect(peak_name, filter_top)]) / sum(conc_rock.ug_g[str_detect(peak_name, filter_bottom)])
) %>%
mutate(peak_name = new_peak_name)
}final_data <- data_by_depth %>%
group_by(file_id) %>%
do({
bind_rows(.,
sum_peaks(., "C15", "all C15"),
sum_peaks(., "C16", "all C16"),
sum_peaks(., "C17", "all C17"),
sum_peaks(., "C18", "all C18"),
sum_peaks(., "C19", "all C19"),
sum_peaks(., "C20", "all C20"),
sum_peaks(., "C21", "all C21"),
sum_peaks(., "C24", "all C24"),
sum_peaks(., "C26", "all C26"),
sum_peaks(., "Aryl", "all_Aryl_Isop"),
#sum_peaks(., "MP", "3ring_MP"),
sum_peaks(., c("Acenapthene", "Flourene"), "2ring_all" ),
#sum_peaks(., "Acenapthene", "2ring_1"),
#sum_peaks(., "Flourene", "2ring_2"),
sum_peaks(., c("Phenanthrene", "Flourantherene", "Retene", "1-MP", "2-MP", "3-MP", "9-MP"), "3ring_all" ),
#sum_peaks(., "Phenanthrene", "3ring_1"),
#sum_peaks(., "Flourantherene", "3ring_2"),
#sum_peaks(., "Retene", "3ring_3"),
sum_peaks(., c("Pyrene", "Benzo[a]anthracene", "Triphenylene", "Chrysene", "flouranthrene"), "4ring_all" ),
#sum_peaks(., "Pyrene", "4ring_1"),
#sum_peaks(., "Benzo[a]anthracene", "4ring_2"),
#sum_peaks(., "Triphenylene", "4ring_3"),
#sum_peaks(., "Chrysene", "4ring_4"),
#sum_peaks(., "flouranthrene", "4ring_5s"),
sum_peaks(., c("Benzo[e]pyrene", "Benzo[a]pyrene", "Perylene", "Ideno[c,e]", "Dibenz[a,h]"), "5ring_all" ),
#sum_peaks(., "Benzo[e]pyrene", "5ring_1"),
#sum_peaks(., "Benzo[a]pyrene", "5ring_2"),
#sum_peaks(., "Perylene", "5ring_3"),
#sum_peaks(., "Ideno[c,e]", "5ring_4"),
#sum_peaks(., "Dibenz[a,h]", "5ring_5"),
sum_peaks(., c("Benzo[ghi]", "Coronene"), "6ring_all" ),
sum_peaks(., c("Acenapthene", "Flourene", "Phenanthrene", "Flourantherene", "Retene", "Pyrene", "Benzo[a]anthracene", "Triphenylene", "Chrysene", "flouranthrene", "Benzo[e]pyrene", "Benzo[a]pyrene", "Perylene", "Ideno[c,e]", "Dibenz[a,h]", "Benzo[ghi]", "Coronene"), "all_PAH")
#sum_peaks(., "Benzo[ghi]", "6ring_1"),
#sum_peaks(., "Coronene", "6ring_2")
) }) %>% ungroup()
final_data## # A tibble: 5,873 x 16
## file_id peak_name RT.min area.cpm height.cts depth rock.g process
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
## 1 TSQ2001_G… 1-MP 26.1 9.69e⁶ 168777000 121 10.7 yes
## 2 TSQ2001_G… 2-MP 26.8 1.26e⁷ 228207000 121 10.7 yes
## 3 TSQ2001_G… 3-MP 26.7 1.47e⁷ 265019000 121 10.7 yes
## 4 TSQ2001_G… 9-MP 25.9 1.09e⁷ 198920000 121 10.7 yes
## 5 TSQ2001_G… Acenapthene 14.2 2.71e⁶ 56677300 121 10.7 yes
## 6 TSQ2001_G… Benzo[a]ant… 41.8 6.84e⁶ 105420000 121 10.7 yes
## 7 TSQ2001_G… Benzo[a]pyr… 51.8 1.51e⁷ 243142000 121 10.7 yes
## 8 TSQ2001_G… Benzo[b]flo… 49.7 1.25e⁷ 146978000 121 10.7 yes
## 9 TSQ2001_G… Benzo[e]pyr… 51.4 1.84e⁷ 300891000 121 10.7 yes
## 10 TSQ2001_G… Benzo[ghi] 60.0 1.66e⁸ 1312680000 121 10.7 yes
## # ... with 5,863 more rows, and 8 more variables: TLE.mg <dbl>,
## # maltenes.mg <dbl>, n_peaks <int>, n_standards <int>,
## # ref_area.cpm <dbl>, ref_amount_added.ug <dbl>, amount.ug <dbl>,
## # conc_rock.ug_g <dbl>Bring in other data for plots
osisotope <- read_excel(file.path("metadata", "SH1_Osi_forGarrett.xlsx")) %>%
rename(depth = `Depth (m)`) # Bring in SH1 data from Jones et al., (2018)
cisotope <- read_excel(file.path("metadata", "Appendix_Table1_geochemistry.xlsx")) %>%
#rename columns
rename(depth = `Abs. depth (m)` , d13c_org = `Average δ13Corg (‰ VPDB)` , carb = `%Carbonate`, TOC = `%TOC`, d13c_carb = `Average δ13Ccarb (‰ VPDB)`) %>%
#remove columns not of interest
select(-`stdev δ13Corg`, -`δ13Ccarb stdev`, -`d18O-avg`, -`d18O stdev`, -`∆13C`, -`d13c_carb`)carb <- cisotope %>%
ggplot() +
aes(depth, carb) +
geom_point() +
geom_line() +
scale_x_reverse() +
coord_flip() +
ggtitle("carbonate%")
TOC <- cisotope %>%
ggplot() +
aes(depth, TOC) +
geom_point() +
geom_line() +
scale_x_reverse() +
coord_flip() +
ggtitle("TOC%") +
theme(axis.text.y = element_blank(),
axis.ticks.y = element_blank(),
axis.title.y = element_blank())
d13c <- cisotope %>%
ggplot() +
aes(depth, d13c_org) +
geom_point() +
geom_line() +
scale_x_reverse() +
coord_flip() +
ggtitle("d13C_org")
os <- osisotope %>%
ggplot() +
aes(x = depth, y = Osi) +
geom_point() +
geom_line() +
#facet_wrap(~peak_name, scales = "free") +
scale_x_reverse() +
coord_flip() +
ggtitle("Os") +
theme(axis.text.y = element_blank(),
axis.ticks.y = element_blank(),
axis.title.y = element_blank())
#ggplotly(carb)
#ggplotly(TOC)
#ggplotly(d13c)
library(gridExtra)##
## Attaching package: 'gridExtra'## The following object is masked from 'package:dplyr':
##
## combinelitho <- grid.arrange(carb, TOC, d13c, os, ncol=4)## Warning: Removed 3 rows containing missing values (geom_point).
By compound
Carotenoids against depth
PZE <- subset(final_data, peak_name %in% c('Chlorobactane', 'Isorenieretane', 'all_Aryl_Isop')) %>%
ggplot() +
aes(x = depth, y = conc_rock.ug_g, color = peak_name) +
geom_point() +
geom_line() +
facet_wrap(~peak_name, scales = "free") +
scale_x_reverse() +
coord_flip() +
ggtitle("PZE")
ggplotly(PZE)#litho <- grid.arrange(d13c, os, PZE, ncol=3)all PAH combos
all <- subset(final_data, peak_name %in% c('all_PAH', '2ring_all', '3ring_all', '4ring_all', '5ring_all', '6ring_all' )) %>%
#filter(!is.na(depth==108)) %>%
ggplot() +
aes(x = depth, y = conc_rock.ug_g, color = peak_name) +
geom_point() +
facet_grid(~peak_name, scales = "free") +
scale_x_reverse() +
coord_flip()
ggplotly(all)Individuals
twos <- subset(final_data, peak_name %in% c("Acenapthene", "Flourene")) %>%
ggplot() +
aes(x = depth, y = conc_rock.ug_g, color = peak_name) +
geom_point() +
geom_line() +
facet_wrap(~peak_name, scales = "free") +
scale_x_reverse() +
coord_flip()
ggplotly(twos)trees <- subset(final_data, peak_name %in% c("Phenanthrene", "Flourantherene", "Retene", "1-MP", "2-MP", "3-MP", "9-MP")) %>%
ggplot() +
aes(x = depth, y = conc_rock.ug_g, color = peak_name) +
geom_point() +
geom_line() +
facet_grid(~peak_name, scales = "free") +
scale_x_reverse() +
coord_flip()
ggplotly(trees)4 ring PAHs
four <- subset(final_data, peak_name %in% c("Pyrene", "Benzo[a]anthracene", "Triphenylene", "Chrysene", "flouranthrene")) %>%
ggplot() +
aes(x = depth, y = conc_rock.ug_g, color = peak_name) +
geom_point() +
geom_line() +
facet_grid(~peak_name, scales = "free") +
scale_x_reverse() +
coord_flip()
ggplotly(four)5 ring PAHs
five <- subset(final_data, peak_name %in% c("Benzo[e]pyrene", "Benzo[a]pyrene", "Perylene", "Ideno[c,e]", "Dibenz[a,h]")) %>%
ggplot() +
aes(x = depth, y = conc_rock.ug_g, color = peak_name) +
geom_point() +
geom_line() +
facet_grid(~peak_name, scales = "free") +
scale_x_reverse() +
coord_flip()
ggplotly(five)6 ring PAHs
six <- subset(final_data, peak_name %in% c("Benzo[ghi]", "Coronene")) %>%
ggplot() +
aes(x = depth, y = conc_rock.ug_g, color = peak_name) +
geom_point() +
geom_line() +
facet_wrap(~peak_name, scales = "free") +
scale_x_reverse() +
coord_flip()
ggplotly(six)all individual PAHs
allind <- subset(final_data, peak_name %in% c("Acenapthene", "Flourene", "Phenanthrene", "Flourantherene", "Retene", "1-MP", "2-MP", "3-MP", "9-MP", "Pyrene", "Benzo[a]anthracene", "Triphenylene", "Chrysene", "flouranthrene", "Benzo[e]pyrene", "Benzo[a]pyrene", "Perylene", "Ideno[c,e]", "Dibenz[a,h]", "Benzo[ghi]", "Coronene")) %>%
ggplot() +
aes(x = depth, y = conc_rock.ug_g, color = peak_name) +
geom_point() +
geom_line() +
facet_grid(~peak_name, scales = "free") +
scale_x_reverse() +
coord_flip()
ggplotly(allind)